AI-Accelerated Product Development
Explainable AI with Layer-wise Relevance Propagation (LRP)
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This recipe is a work in progress with the goal of providing curated and hands on resources for various aspects of explainable AI.
Potential Use Cases
blackbox model interpretation, model risk assessment, model bias reduction
Who is This For ?
Data Scientists, Machine Learning Engineers
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1. Explainable AI Introduction
What is this recipe and how can i use it?
2. The Need for Machine Learning Explanations
Why Do We Need Machine Learning Explanations?
3. Explainability Options
What are various ways we can explain ML models?
4. Local interpretable model-agnostic explanations
5. SHAP (SHapley Additive exPlanations)
6. XAI Reading List
Where can i read more about explainable AI?
7. XAI Tools
What tools can I use for explainable AI?
8. Interpretability through Model-Agnostic Methods
What options do I have to add interpratbility to existing models?
9. Explainable AI with Layer-wise Relevance Propagation (LRP)
10. Explaining image classifiers by removing input features using generative models
11. Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples
12. TF-Encrypted: Private machine learning in tensorflow with secure computing
13. Explaining by Removing: A Unified Framework for Model Explanation
14. High-frequency Component Helps Explain the Generalization of Convolutional Neural Networks
1 week ago
Thank you for providing such curated study resources